GSTL: the geostatistical template library in C++
Identifieur interne : 000856 ( PascalFrancis/Corpus ); précédent : 000855; suivant : 000857GSTL: the geostatistical template library in C++
Auteurs : Nicolas Remy ; Arben Shtuka ; Bruno Levy ; Jef CaersSource :
- Computers & geosciences [ 0098-3004 ] ; 2002.
Descripteurs français
- Pascal (Inist)
English descriptors
Abstract
The development of geostatistics has been mostly accomplished by application-oriented engineers in the past 20 years. The focus on concrete applications gave birth to many algorithms and computer programs designed to address different issues, such as estimating or simulating a variable while possibly accounting for secondary information such as seismic data, or integrating geological and geometrical data. At the core of any geostatistical data integration methodology is a well-designed algorithm. Yet, despite their obvious differences, all these algorithms share many commonalities on which to build a geostatistics programming library, lest the resulting library is poorly reusable and difficult to expand. Building on this observation, we design a comprehensive, yet flexible and easily reusable library of geostatistics algorithms in C ++. The recent advent of the generic programming paradigm allows us elegantly to express the commonalities of the geostatistical algorithms into computer code. Generic programming, also referred to as "programming with concepts", provides a high level of abstraction without loss of efficiency. This last point is a major gain over object-oriented programming which often trades efficiency for abstraction. It is not enough for a numerical library to be reusable, it also has to be fast. Because generic programming is "programming with concepts", the essential step in the library design is the careful identification and thorough definition of these concepts shared by most geostatistical algorithms. Building on these definitions, a generic and expandable code can be developed. To show the advantages of such a generic library, we use GSTL to build two sequential simulation programs working on two different types of grids-a surface with faults and an unstructured grid without requiring any change to the GSTL code.
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Format Inist (serveur)
| NO : | PASCAL 02-0513659 INIST |
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| ET : | GSTL: the geostatistical template library in C++ |
| AU : | REMY (Nicolas); SHTUKA (Arben); LEVY (Bruno); CAERS (Jef) |
| AF : | Department of Geological and Environmental Sciences, Stanford University, Braun Hall, Building 320/Stanford, CA 94305-2115/Etats-Unis (1 aut.); Ecole Superieure de Geologie, A.S.G.A.-Gocad, Batiment G. BP 40-rue du Doyen Marcel Roubault/54501 Vandoeuvre-les-Nancy/France (2 aut.); Institut lie Recherche en Informatique et en Automatique, INRIA Lorraine/Loria, Technopfle de Nancy Brabois-Campus scientifique, 615, rue du Jardin Botanique/54602 Villers-les-Nancy/France (3 aut.); Department of Petroleum Engineering, Stanford University/Stanford, CA 94305-2220/Etats-Unis (4 aut.) |
| DT : | Publication en série; Niveau analytique |
| SO : | Computers & geosciences; ISSN 0098-3004; Royaume-Uni; Da. 2002; Vol. 28; No. 8; Pp. 971-979; Bibl. 20 ref. |
| LA : | Anglais |
| EA : | The development of geostatistics has been mostly accomplished by application-oriented engineers in the past 20 years. The focus on concrete applications gave birth to many algorithms and computer programs designed to address different issues, such as estimating or simulating a variable while possibly accounting for secondary information such as seismic data, or integrating geological and geometrical data. At the core of any geostatistical data integration methodology is a well-designed algorithm. Yet, despite their obvious differences, all these algorithms share many commonalities on which to build a geostatistics programming library, lest the resulting library is poorly reusable and difficult to expand. Building on this observation, we design a comprehensive, yet flexible and easily reusable library of geostatistics algorithms in C ++. The recent advent of the generic programming paradigm allows us elegantly to express the commonalities of the geostatistical algorithms into computer code. Generic programming, also referred to as "programming with concepts", provides a high level of abstraction without loss of efficiency. This last point is a major gain over object-oriented programming which often trades efficiency for abstraction. It is not enough for a numerical library to be reusable, it also has to be fast. Because generic programming is "programming with concepts", the essential step in the library design is the careful identification and thorough definition of these concepts shared by most geostatistical algorithms. Building on these definitions, a generic and expandable code can be developed. To show the advantages of such a generic library, we use GSTL to build two sequential simulation programs working on two different types of grids-a surface with faults and an unstructured grid without requiring any change to the GSTL code. |
| CC : | 225B04; 001E01M04 |
| FD : | Programme ordinateur; Géostatistique; Simulation; Modèle stochastique; Géométrie; Algorithme; Estimation |
| ED : | computer programs; geostatistics; simulation; stochastic models; geometry; algorithms |
| SD : | Programa computador; Geoestadística; Simulación; Geometría; Algoritmo |
| LO : | INIST-17165.354000104828680110 |
| ID : | 02-0513659 |
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Pascal:02-0513659Le document en format XML
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BP 40-rue du Doyen Marcel Roubault</s1><s2>54501 Vandoeuvre-les-Nancy</s2><s3>FRA</s3><sZ>2 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Levy, Bruno" sort="Levy, Bruno" uniqKey="Levy B" first="Bruno" last="Levy">Bruno Levy</name><affiliation><inist:fA14 i1="03"><s1>Institut lie Recherche en Informatique et en Automatique, INRIA Lorraine/Loria, Technopfle de Nancy Brabois-Campus scientifique, 615, rue du Jardin Botanique</s1><s2>54602 Villers-les-Nancy</s2><s3>FRA</s3><sZ>3 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Caers, Jef" sort="Caers, Jef" uniqKey="Caers J" first="Jef" last="Caers">Jef Caers</name><affiliation><inist:fA14 i1="04"><s1>Department of Petroleum Engineering, Stanford University</s1><s2>Stanford, CA 94305-2220</s2><s3>USA</s3><sZ>4 aut.</sZ></inist:fA14></affiliation></author></analytic><series><title level="j" type="main">Computers & geosciences</title><title level="j" type="abbreviated">Comput. geosci.</title><idno type="ISSN">0098-3004</idno><imprint><date when="2002">2002</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Computers & geosciences</title><title level="j" type="abbreviated">Comput. geosci.</title><idno type="ISSN">0098-3004</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>algorithms</term><term>computer programs</term><term>geometry</term><term>geostatistics</term><term>simulation</term><term>stochastic models</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Programme ordinateur</term><term>Géostatistique</term><term>Simulation</term><term>Modèle stochastique</term><term>Géométrie</term><term>Algorithme</term><term>Estimation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The development of geostatistics has been mostly accomplished by application-oriented engineers in the past 20 years. The focus on concrete applications gave birth to many algorithms and computer programs designed to address different issues, such as estimating or simulating a variable while possibly accounting for secondary information such as seismic data, or integrating geological and geometrical data. At the core of any geostatistical data integration methodology is a well-designed algorithm. Yet, despite their obvious differences, all these algorithms share many commonalities on which to build a geostatistics programming library, lest the resulting library is poorly reusable and difficult to expand. Building on this observation, we design a comprehensive, yet flexible and easily reusable library of geostatistics algorithms in C ++. The recent advent of the generic programming paradigm allows us elegantly to express the commonalities of the geostatistical algorithms into computer code. Generic programming, also referred to as "programming with concepts", provides a high level of abstraction without loss of efficiency. This last point is a major gain over object-oriented programming which often trades efficiency for abstraction. It is not enough for a numerical library to be reusable, it also has to be fast. Because generic programming is "programming with concepts", the essential step in the library design is the careful identification and thorough definition of these concepts shared by most geostatistical algorithms. Building on these definitions, a generic and expandable code can be developed. To show the advantages of such a generic library, we use G<sub>S</sub>TL to build two sequential simulation programs working on two different types of grids-a surface with faults and an unstructured grid without requiring any change to the GSTL code.</div></front></TEI><inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0098-3004</s0></fA01><fA03 i2="1"><s0>Comput. geosci.</s0></fA03><fA05><s2>28</s2></fA05><fA06><s2>8</s2></fA06><fA08 i1="01" i2="1" l="ENG"><s1>G<sub>S</sub>TL: the geostatistical template library in C++</s1></fA08><fA11 i1="01" i2="1"><s1>REMY (Nicolas)</s1></fA11><fA11 i1="02" i2="1"><s1>SHTUKA (Arben)</s1></fA11><fA11 i1="03" i2="1"><s1>LEVY (Bruno)</s1></fA11><fA11 i1="04" i2="1"><s1>CAERS (Jef)</s1></fA11><fA14 i1="01"><s1>Department of Geological and Environmental Sciences, Stanford University, Braun Hall, Building 320</s1><s2>Stanford, CA 94305-2115</s2><s3>USA</s3><sZ>1 aut.</sZ></fA14><fA14 i1="02"><s1>Ecole Superieure de Geologie, A.S.G.A.-Gocad, Batiment G. 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All rights reserved.</s1></fA44><fA45><s0>20 ref.</s0></fA45><fA47 i1="01" i2="1"><s0>02-0513659</s0></fA47><fA60><s1>P</s1></fA60><fA61><s0>A</s0></fA61><fA64 i1="01" i2="1"><s0>Computers & geosciences</s0></fA64><fA66 i1="01"><s0>GBR</s0></fA66><fC01 i1="01" l="ENG"><s0>The development of geostatistics has been mostly accomplished by application-oriented engineers in the past 20 years. The focus on concrete applications gave birth to many algorithms and computer programs designed to address different issues, such as estimating or simulating a variable while possibly accounting for secondary information such as seismic data, or integrating geological and geometrical data. At the core of any geostatistical data integration methodology is a well-designed algorithm. Yet, despite their obvious differences, all these algorithms share many commonalities on which to build a geostatistics programming library, lest the resulting library is poorly reusable and difficult to expand. Building on this observation, we design a comprehensive, yet flexible and easily reusable library of geostatistics algorithms in C ++. The recent advent of the generic programming paradigm allows us elegantly to express the commonalities of the geostatistical algorithms into computer code. Generic programming, also referred to as "programming with concepts", provides a high level of abstraction without loss of efficiency. This last point is a major gain over object-oriented programming which often trades efficiency for abstraction. It is not enough for a numerical library to be reusable, it also has to be fast. Because generic programming is "programming with concepts", the essential step in the library design is the careful identification and thorough definition of these concepts shared by most geostatistical algorithms. Building on these definitions, a generic and expandable code can be developed. 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BP 40-rue du Doyen Marcel Roubault/54501 Vandoeuvre-les-Nancy/France (2 aut.); Institut lie Recherche en Informatique et en Automatique, INRIA Lorraine/Loria, Technopfle de Nancy Brabois-Campus scientifique, 615, rue du Jardin Botanique/54602 Villers-les-Nancy/France (3 aut.); Department of Petroleum Engineering, Stanford University/Stanford, CA 94305-2220/Etats-Unis (4 aut.)</AF><DT>Publication en série; Niveau analytique</DT><SO>Computers & geosciences; ISSN 0098-3004; Royaume-Uni; Da. 2002; Vol. 28; No. 8; Pp. 971-979; Bibl. 20 ref.</SO><LA>Anglais</LA><EA>The development of geostatistics has been mostly accomplished by application-oriented engineers in the past 20 years. The focus on concrete applications gave birth to many algorithms and computer programs designed to address different issues, such as estimating or simulating a variable while possibly accounting for secondary information such as seismic data, or integrating geological and geometrical data. At the core of any geostatistical data integration methodology is a well-designed algorithm. Yet, despite their obvious differences, all these algorithms share many commonalities on which to build a geostatistics programming library, lest the resulting library is poorly reusable and difficult to expand. Building on this observation, we design a comprehensive, yet flexible and easily reusable library of geostatistics algorithms in C ++. The recent advent of the generic programming paradigm allows us elegantly to express the commonalities of the geostatistical algorithms into computer code. Generic programming, also referred to as "programming with concepts", provides a high level of abstraction without loss of efficiency. This last point is a major gain over object-oriented programming which often trades efficiency for abstraction. It is not enough for a numerical library to be reusable, it also has to be fast. Because generic programming is "programming with concepts", the essential step in the library design is the careful identification and thorough definition of these concepts shared by most geostatistical algorithms. Building on these definitions, a generic and expandable code can be developed. To show the advantages of such a generic library, we use G<sub>S</sub>TL to build two sequential simulation programs working on two different types of grids-a surface with faults and an unstructured grid without requiring any change to the GSTL code.</EA><CC>225B04; 001E01M04</CC><FD>Programme ordinateur; Géostatistique; Simulation; Modèle stochastique; Géométrie; Algorithme; Estimation</FD><ED>computer programs; geostatistics; simulation; stochastic models; geometry; algorithms</ED><SD>Programa computador; Geoestadística; Simulación; Geometría; Algoritmo</SD><LO>INIST-17165.354000104828680110</LO><ID>02-0513659</ID></server></inist></record>Pour manipuler ce document sous Unix (Dilib)
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